Good to know. Will look into the Raspberry Pi 4 (w/4GB RAM).
In general, are there any tuning or configuration tips/tricks for very
memory-constrained deployments (e.g., 1-4GB RAM)?
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We are trying to benchmark TPC-H (scale factor 1) on a 13-node Raspberry Pi
3B+ cluster (1 master, 12 workers). Each node has 1GB of RAM and a quad-core
processor, running Ubuntu Server 18.04. The cluster is using the Spark
standalone scheduler with the *.tbl files from TPCH’s dbgen tool stored in
Thanks for your reply, but I'm still running into issues
installing/configuring the native libraries for MLlib. Here are the steps
I've taken, please let me know if anything is incorrect.
- Download Spark source
- unzip and compile using `mvn -Pnetlib-lgpl -DskipTests clean package `
- Run
Hi, i'm trying to improve performance for Spark's Mllib, and I am having
trouble getting native netlib-java libraries installed/recognized by Spark.
I am running on a single machine, Ubuntu 14.04 and here is what I've tried:
sudo apt-get install libgfortran3
sudo apt-get install libatlas3-base
I am running it in local. How can I use the built version (in local mode) so
that I can use the native libraries?
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Hi, I was wondering how to implement fixed sized strings in Spark SQL. I
would like to implement TPC-H, which uses fixed sized strings for certain
fields (i.e., 15 character L_SHIPMODE field).
Is there a way to use a fixed length char array instead of using a string?
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Hey, I'm trying to run TPC-H Query 4 (shown below), and get the following
error:
Exception in thread main java.lang.RuntimeException: [11.25] failure:
``UNION'' expected but `select' found
It seems like Spark SQL doesn't support the exists clause. Is this true?
select
o_orderpriority,
